Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Information-Theoretic Generalization Bounds for Deep Neural Networks
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.03176.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:21:45.297531Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T05:32:05.708486Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 62a59e50-a156-45bd-98d2-bd5be611d41f · inbound
Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel cites this paper.
Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel Information-Theoretic Generalization Bounds for Deep Neural Networks
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:21:45.297531Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3bb7ee6b-408d-4557-866b-e42674c415fb · inbound
The Generalization Ridge: Information Flow in Natural Language Generation cites this paper.
The Generalization Ridge: Information Flow in Natural Language Generation Information-Theoretic Generalization Bounds for Deep Neural Networks
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T05:32:05.711802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6e77d309-0849-4fbf-a542-43495812efc7 · inbound
An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning cites this paper.
An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning Information-Theoretic Generalization Bounds for Deep Neural Networks
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T03:52:22.080884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.